Companies in the restaurant, retail, travel, hospitality, and entertainment industries are constantly seeking new promotions for attracting new customers, moving them through the purchasing process more efficiently, increasing revenues, and improving customer loyalty. Additionally, these entities also constantly seek new ways to improve customer retention by obtaining more precise data about their customer's preferences that may be used to target market those customers. In recent years, technology has been used to implement new ways to attract customers and to create store-specific customer profiles and the like for purposes of improving target marketing. The Internet has facilitated such efforts by enabling data management of the collected customer information. However, target marketing efforts to data have been relatively ineffective as they group, score, and mine the customer data but do not effectively reach each customer at the appropriate time and place, instead using broad advertising calls, mailers, e-mails, surveys, and other methods of contacting the customer with general marketing campaigns. In short, the methods to date have not enabled vendors to gather the depth of information that they need in order to specifically tailor offers to meet each customer's needs while accounting for the customer's buying preferences. Better knowledge of the customer's specific purchasing and profile preference information will allow vendors to send precision offers to customers based on real-time purchasing and profile preference data of that customer. Moreover, an approach is needed whereby the precision offers reach the customers at the appropriate time (e.g., when the customers are in the proximity of the vendor's location) in the way that the customer prefers to receive such offers. For example, the system described herein reaches customers via a web-based profile account within a closed social network and network of vendors by contacting the customers with alerts in the form of a text, email, phone call, or other form of communication.
Conventionally, credit card companies collect empirical data from a customer's purchase history by scoring, which incorporates purchases made by the customer, to predict, based on probability, future purchases by that customer. The vendors, and sometimes the credit card merchants, sell this information after collecting the customer purchase information via scoring. The scores can take many different forms, from number signs to strings of entire data structures, but the most common are numerical scores. For example, in U.S. Pat. No. 7,328,169, a system is described that collects data about purchases by groups or “clusters” of customers and makes predictions of customer behavior based on the expected actions of members of the clusters using complicated algorithms.
Other companies specialize in loyalty programs based on transaction histories. For example, U.S. Pat. No. 6,119,933 describes a rewards program based on a customer's past transactional history at retailers within a network. One application of this method is in the form of a rewards card known as a retail “smart card.” The vendors may access some of the customer purchase information based on purchases made at the vendors' stores. However, such a system does not include the purchasing preference data of the customer and does not incorporate purchasing information from other industries in order to create a more robust purchase preference profile. On the contrary, the collected information is generally limited to demographic information and purchase information when goods are purchased from specific vendors.
Another type of loyalty rewards program used by some banks is related to scoring data based on a purchase “relationship.” The rewards include reduced loan rates, increased deposit amounts, and incentives. Management compiles reports based on this data and used the reports in the marketing efforts of the bank. This sort of internal marketing program is used for the specific purpose of reaching a customer when they have a specified credit rating or are in a particular financial position.
Other methods, such as those described in U.S. Pat. No. 6,039,244, are used to reward customers for repeat purchases. Customer purchase data is collected and used to generate rebates and coupons by identifying each customer with a unique customer code that remains invisible to the customer. This system uses a label or tag instead of a specific client username/account number for identifying the customer.
Still other marketing methods include the methods described in U.S. Pat. No. 5,930,764, whereby a customer's bank history is used to reach the customer for financial marketing campaigns. These records are also used to track sales and service performance of internal operations. The collected information is housed internally and organized according to geographic region and other such groupings. The information is gathered using queries from bank recorded transactions and the results are housed in a central database system for sharing with other internal departments for purposes of marketing campaigns. The data is not shared with the customers and does not enable customers to utilize customer preference data of other customers within their business and/or personal network of contacts for purposes of, for example, buying gifts for the other person.
Existing systems allow customers to customize their profiles with items from any vendor within the network of vendors at his/her choice. Also, some methods, such as those described in U.S. Pat. Nos. 5,933,827 and 5,999,975, link websites and users into categories in order to search for potential matches of what vendor or site the customer may like if they like another vendor or site. However, rather than using such automated matching systems, it is desirable to leave it up to the customer to group and customize the web-based application of the profile system to his/her own business/personal preference of grouping vendors, web pages, and users and to add vendors and websites to his/her web-based profile account within a closed social network and network of vendors.
Another method of calculating user choices in the prior art is based on probability of previous user choices by way of ranking popularity. Thus, if the known data ranks at the top of the priority, then a system of probability will add relevant matches using collaborative filters. Such a system is described, for example, in U.S. Pat. No. 6,006,218. It is desired to instead use “controllable parameters” whereby the user determines exactly what vendors, items, and privacy options for other users that they wish to set themselves for their business/personal profile. In addition, it is desirable for the customer to be able to choose the popularity of vendors and users within his/her network based upon, for instance, events they have scheduled, users that they have recently held conversations with, and other selections that he/she has chosen within his her business/personal interests within a web-based application.
Yet another approach to collaborative advertising/marketing in the prior art is in the form of the automated filtering of users into a community based on other users with similar interests. More specifically, U.S. Pat. No. 5,918,014 describes a system in which users that view a certain site on the World Wide Web simultaneously will be offered the same advertisement by means of “cookies.” However, it is desirable for the user to choose with whom he/she would like to be associated within a social network, for instance, by way of the web-based application of his/her business/personal profile. In addition, it is desirable that recent purchases and personal preference updates be what triggers offers into his/her account. For example, if a user chooses a flight to a city and stays at a hotel within the “network” and perhaps dines at a restaurant or participates in an entertainment event within the “network,” the user may be offered a return visit and may set how often he/she would like to receive offers to his/her profile account. In the same example, it may be desirable for the user to be offered a similar “trip” to a similar city, whereby he/she may be offered incentives to choose similar restaurant, retail, travel, hospitality, and/or entertainment offers from vendors within the “network.” Again, it is desirable for the user to set who may contact him/her by way of his/her web-based profile within a particular window of time designated by the user.
A different type of collaborative filtering, similar to Amazon's customer “Who Bought” feature, is described in U.S. Pat. No. 6,041,311. This method of filtering is stored into the “memory” of user profiles. The item is stored with the user's rating of the item. When a similar item becomes available, the user is presented with an offer. Users can be grouped, then, into clusters based on the plurality of his/her selections. It is desirable to use a similar method to gather customer data whereby the customer may choose what items he/she values as “popular” and may modify his/her “popular” picks over time. However, it is also desirable that each user further have the ability to set who they may be grouped with and what vendors he/she would like to receive offers from within the “network.” In other words, it is desirable that the grouping be specific to the item(s) or category of item(s) from any number of vendors in any area of business, restaurant, retail, travel, hospitality, and/or entertainment, within the “network.” Also, if a user wishes to travel to a new destination, based on the user's restaurant, retail, travel, hospitality, and entertainment preferences in his/her profile, the user may wish to receive package offers that he/she may also like. Moreover, it may be desirable for a package offer to include types of restaurants, retail, travel, hospitality, entertainment, and a combination thereof that are customized to the user's specific preference choices and previous purchase history. Although the offers may be made by grouping choices of previous preferences and purchases, the offers may be customized based on the user's profile information.
In addition, there are a handful of applications, such as described in US Patent Application 2006/0136589, where a computer generated profile system is used to make recommendations based on probability calculations, instead of finite preferences. However, such an approach requires complicated algorithms and does not effectively integrate actual customer choices.
Other prior art methods of marketing that include the use of customer purchase history shared in a community are limited to purchases only. For example, US Patent Application 2005/0261987 is a good example of a content-based system of filtering in a collaborative environment where the community can see all or a limited purchase history in order to find products with which the user has interest. However, this sort of group is limited to purchases that the user has made and often does not give the user the ability to choose who may see what data or, more specifically, who is in his/her “community.”
Still other prior art methods, such as described in US Patent Application 2003/0216956, build customer preferences by way of questionnaires. With this method, a customer is given further insight to his/her purchase history by way of answering questions detailed in the questionnaires. Although this method gathers information based on transaction history, it does not access customer preferences in a convenient manner. For example, it is desirable to enable a user to choose to purchase a pair of pants from a store whereby the user is offered items in the same category according to his/her personal criteria which he/she has detailed in his/her profile. A shirt of the same line, detailed to his/her shirt cut, style, color, and other preferences, may be offered to accompany the order. Therefore, the user has customized his/her preferences in his/her profile in order to avoid items that are not related to his/her preferences. With no questionnaire needed, the customer could input directly his/her preferences.
Another prior art method of marketing is based on offering incentives and determining if the incentives were redeemed in a tracking system, as is the case in US Patent Application 2003/0158776. These incentives are tracked by way of proof of purchase and “cookies.” This is a very complicated system that has very little input from the customer. It is another method of predicting what the customer may like based on what offers have been accepted. It is desirable that the customer instead be offered incentives based on a combination of his/her personal preferences detailed in his/her profile and purchase history and that vendors send offers that are popular to the customer rather than predicting what the user likes based on redeemed offers. The logic is similar whereby a general offer may be sent to the customer in order to induce a repeat purchase. However, it is desired that the preferences be compiled in a system by the customer based on the customer's purchases. For example, if during a recent visit to a restaurant within the “network,” a customer purchased a piece of cheesecake, it is desired that the offer that he/she may receive from the vendor from whom he/she has recently made the purchase, as well as all other customers that have elected to make purchases from or build a profile for this vendor's restaurant within the “network” by way of the invention's database, may be an offer for a “sign-up a friend” marketing campaign. In this offer, he/she would be offered his/her favorite, most recently purchased, or other criteria specified by the restaurant vendor, piece of cheesecake when the customer refers a friend to create a profile. In this case, the customer's follow-up could be used for future offers or the like by this or all vendors within the “network.” As another example, the original customer could sent an e-vite through the system's web application to tell a friend about the offer and add his/her comments to the invitation. The original customer may also receive an incentive built into the vendor's promotional offer when the new member accepts and registers an account.
An example of data mining of purchase information in a prior art system is described in US Patent Application 2003/0088491. The intent of that method is to create cross-promotional marketing opportunities by creating “association rules” and “profit levels” for each product. However, it is desirable that the association of the cross promotional marketing opportunities be paired in a much different way. For example, it is desirable to provide a method to (1) allow vendors within the “network” to join together, for a single or multiple campaigns, in order to reach a specific set of users based on their elected, collaborative criteria, and (2) allow the user to use the customer preference data within the customer-built profile database system to consult companies within and outside of the “network” in order to offer business consulting on company campaigns and the opportunity to invite other businesses to join the “network” for cross-promotional marketing opportunities. Unlike many systems, however, this information would preferably not be sold or shared within any company outside of the “network.”
Possibly the broadest method of grouping customer data as it relates to the current invention is detailed in US Patent Application 2003/0061132. In this method, categorizing, aggregating, and analyzing consumer and business payment transaction data according to geographic, demographic, topological, meteorological, chronological, and other parameters for analysis are used in different industries of commerce to assess customer's future purchases based on purchase amounts, rather than by actual items purchased. It is desirable to further market to the customer based on his/her profile preferences and purchase history within the “network” of vendors and vendor products.
Travel companies, such as Expedia, Hotwire, Tripit, and the like, present offers to customers who have trips to destinations within their “network.” They offer trip packages to customers based on his/her previous purchase history. Sometimes these offers will be accompanied by offers within their stock of promotions from their “partners.” However, in these systems, customers do not build a total profile detailing services and items that the customer prefers. In addition, the marketing offers that the customer receives is a “bottom-of-the-barrel” or “last minute discount” offer, instead of a specifically tailored offer that meets all of the customer-preferred criterion. Moreover, the medium with which this method is used is via e-mail offers. It is desirable that the system instead utilize each customer's business/personal profile in order to provide the customer with offers that meet all of his/her preferred criteria and/or past purchase transactional history. Going beyond virtual personal assistant solutions, it is desired to market to each customer based on a combination of his/her past purchases and current Profile preferences in the areas of restaurant, retail, travel, hospitality, entertainment, and a combination thereof by sending relevant offers to the customer based on his/her profile so that he/she may access them onsite and/or online using web-based applications via a mobile device, onsite touch screens, stand-alone kiosk touch screens, and all other methods of accessing a web-based profile database.
A system is further desired whereby a customer may be presented with giving options, whereby a customer can gift a pair of pants, for example, to another customer within his/her approved network of contacts and accurately choose the size, color, cut, and other specific preference information of another customer without needing to know the customer's personal information, such as street name, banking institution, and other such private information. Therefore, a system is desired that reaches beyond capturing and organizing customer data based on purchases alone. A system is also desired that gives customers the ability to utilize the customer preference data of other customers within their business/personal network of contacts for the purposes of providing personalized gifts in the areas of restaurant, retail, travel, hospitality, and entertainment. A system that supports marketing campaigns that are tailored to a combination of each customer's purchases and personal profile data that is used for the purposes of consumer purchasing remains desired.
The system described herein further contemplates the use of a cardless payment system utilizing a customer's profile information. For example, the Precision Gifting™ and Precision Purchasing™ systems described herein use methods of purchasing via a customer's user name and biometric scan verification at a stand-alone kiosk, vendor locations, and touch screens in airplanes and the like in order to access the customer database to make purchases and provide gifts to other customers in the areas of restaurant, travel, hospitality, entertainment, shopping, and a combination thereof. Though used for different purposes, several “cardless” payment systems may be found in the prior art. For example, a cardless transaction system is described in US Patent Publication 2007/0083400 whereby a user is preauthorized to pay for a desired service by credit and the authorization is forwarded to a point-of-sale system at the point of purchase. Other cardless transaction systems, such as those described in U.S. Pat. No. 7,006,986 and U.S. Pat. No. 7,080,048, use verification parameters of the customer's account information to validate a merchant transaction for payment. Verification of electronic transactions may also be provided using biometrics, as described in U.S. Pat. No. 6,920,435, U.S. Pat. No. 6,950,810, and U.S. Pat. No. 6,286,756, or may be tied to the customer's telephone number, as in U.S. Pat. No. 6,227,447 and U.S. Pat. No. 6,341,724. Cashless gaming systems, such as those described in U.S. Pat. No. 6,585,598 and U.S. Pat. No. 6,739,975, also permit wireless cash transfers over wireless communications devices using identifying information of the individual and a PIN number, thus enabling use of a particular gaming machine. Adaptation of such systems for use with the customer profile system described herein is desired.
A system and method is desired that meets the above and other related needs in the art.